In this study, we proposed multiple local features encoded for recognizing the human actions. The multiple local features were obtained from the simple feature description of human actions in video. The simple features are two kinds of important features, optical flow and edge, to represent the human perception for the video behavior. As the video information descriptors, optical flow and edge, which their computing speeds are very fast and their requirement of memory consumption is very low, can represent respectively the motion information and shape information. Furthermore, key local multi-features are extracted and encoded by GA in order to reduce the computational complexity of the algorithm. After then, the Multi-SVM classifier is applied to discriminate the human actions.